Effective business intelligence and qualitative data analysis. business intelligence

Everyone big business and most medium-sized structures are faced with the problem of providing management with inaccurate data on the state of the company. The reasons may be different, but the consequences are always the same - wrong or untimely decisions that adversely affect the effectiveness of financial transactions. To avoid such situations, a professional business intelligence or BI system is designed ( from English. - business intelligence ). These high-tech "assistants" contribute to building a system of managerial control of every aspect within the business.

At its core, BI systems are advanced analytical software for business analysis and reporting. These programs can use data from various sources of information and provide them in a convenient form and section. As a result, management gets quick access to complete and transparent information about the state of affairs of the company. A feature of the reports obtained with the help of BI is the ability of the manager to independently choose in which context to obtain information.

Modern Business Intelligence systems are multifunctional. That is why in large companies they are gradually replacing other ways of obtaining business reporting. Their main capabilities include:

• Connections to various databases, in particular, to;
• Formation of reports of varying complexity, structure, type and layout at high speed. It is also possible to set a schedule for generating reports on a schedule without direct participation and data distribution;
• Transparent work with data;
• Ensuring a clear link between information from different sources;
• Flexible and intuitive configuration of access rights for employees in the system;
• Saving data in any format convenient for you - PDF, Excel, HTML and many others.

The capabilities of business intelligence information systems allow the manager not to depend on the IT department or his assistants to submit the required information. It is also a great opportunity to demonstrate the right direction of your decisions not with words, but with exact numbers. Many large network corporations in the West have been using BI systems for a long time, including the world-famous Amazon, Yahoo, Wall-Mart, etc. The above-mentioned corporations spend decent money on business intelligence, but the implemented BI systems bring invaluable benefits.

The benefits of professional business intelligence systems are based on principles that are supported in all advanced BI applications:

1. visibility. The main interface of any business analysis software should reflect key metrics. Thanks to this, the manager will quickly be able to assess the state of affairs in the enterprise and begin to do something if necessary;
2. Customization. Each user should be able to customize the interface and function keys in the most convenient way for themselves;
3. Layering. Each data set should have several cuts (layers) to provide the detail of information that is needed at a particular level;
4. Interactivity. Users should be able to collect information from all sources and in several directions at the same time. It is necessary that the system has the function of setting alerts by key parameters;
5. Multithreading and access control. The BI system should be able to implement simultaneous work of a large number of users with the ability to set different access levels for them.

The entire IT community agrees that Information Systems business intelligence is one of promising areas industry development. However, their implementation is often hampered by technical and psychological barriers, uncoordinated work of managers and the lack of prescribed areas of responsibility.

When considering the implementation of class BI systems, it is important to remember that the success of the project will largely depend on the attitude of the company's employees to the innovation. This applies to all IT products: skepticism and fear of downsizing can thwart all implementation efforts. Therefore, it is very important to understand how the business intelligence system makes future users feel. The ideal situation will be when the company's employees will treat the system as an assistant and a tool for improving work.

Before starting a project to introduce BI technology, it is necessary to conduct a thorough analysis of the company's business processes and the principles of managerial decision-making. After all, it is these data that will be involved in the analysis of the situation in the company. It will also help to make a choice of a BI system along with other main criteria:

1. Goals and objectives of the implementation of BI systems;
2. Requirements for data storage and the ability to operate with them;
3. Data integration functions. Without using data from all sources in the company, management will not be able to get a holistic picture of the state of affairs;
4. Visualization capabilities. For each person, the ideal BI analytics looks different, and the system must meet the needs of each user;
5. Universality or narrow specialization. In the world there are systems aimed at a certain industry, and universal solutions, allowing to collect information in any context;
6. Requirement for resources and price for software. The choice of a BI system, like any software, depends on the capabilities of the company.

The above criteria will help management make an informed choice among the variety of known business intelligence systems. There are other parameters (for example, data storage structure, web architecture), but they require skills in narrow IT areas.

It is not enough just to make a choice, buy software, install and configure it. Successful implementation of BI systems of any direction is based on the following rules:

• Data correctness. If the data for analysis is incorrect, then there is a possibility of a serious system error;
• Full training for each user;
• Rapid implementation. It is necessary to focus on the correct formation of the necessary reports in all key places, and not on the ideal service for one user. Adjust appearance report or add another section for convenience, you can always after implementation;
• Understand the return on investment in your BI system. The effect depends on many factors and in some cases is visible only after a few months;
• Equipment must be designed not only for current situation but also for the near future;
• Understand why the BI implementation was started, and do not demand the impossible from the software.

According to statistics, only 30% of company executives are satisfied with the implementation of BI systems. Over the long years of the existence of software for business analysis, experts have formulated 9 key mistakes, which can reduce efficiency to a minimum:

1. Non-obviousness of the purpose of implementation for management. Often the project is created by the IT department without the close participation of managers. In most cases, in the process of implementation and operation, questions arise regarding the purpose and objectives of the BI system, the benefits and ease of use;
2. Lack of transparency in management, work of employees and decision-making. Managers may not know how employees work in the field, and management decisions can be accepted not only on the basis of dry facts. This will lead to the impossibility of maintaining the existing paradigm as a result of the implementation of the BI system. And often break the culture that has developed over the years corporate governance impossible;
3. Insufficient reliability of data. It is unacceptable for false information to enter the business analysis system, otherwise employees will not be able to trust it and use it;
4. Wrong choice of a professional business intelligence system. Many examples in history when management hires a third-party organization to implement a BI system and does not take part in its selection speak for themselves. As a result, a system is being introduced that does not allow obtaining the required report or with which it is impossible to integrate one of the existing software in the company;
5. Lack of a plan for the future. The peculiarity of BI systems is that it is not static software. It is impossible to finish an implementation project and not think about it. There are many requirements from users and management in terms of improvements;
6. Transfer of the BI system to a third-party organization for support. As practice shows, most often such situations lead to the isolation of the product and the isolation of the system from the real state of affairs. Own support service responds much faster and more efficiently to user feedback and management requirements;
7. Desire to save. In business, this is normal, but BI analytics only works if it takes into account all aspects of the company's activities. That is why deep analytical systems with high cost are the most effective. The desire to receive several reports on areas of interest leads to common mistakes in data and great dependence on the qualifications of IT specialists;
8. Different terminology in the company. It is important that all users understand the basic terms and their meaning. A simple misunderstanding can lead to misinterpretation of the reports and indicators of the BI system;
9. Lack of a unified strategy for business analysis in the enterprise. Without a single course selected for all employees, any BI class system will be just a set of disparate reports that meet the requirements of individual managers.

Implementation of BI systems - important step to help take your business to the next level. But this will require not only a fairly large infusion of finance, but also the time and effort of each employee of the company. Not every business is ready to competently complete the project of implementing a business analysis system.

(Business Intelligence).

As speakers at the seminar are invited young professionals who make successful career analysts in high-tech companies such as Microsoft, IBM, Google, Yandex, MTS, etc. At each seminar, students are told about some business tasks that are solved in these companies, about how data is accumulated, how analysis tasks arise data and how they can be solved.

All invited specialists are open for contacts, and students will be able to contact them for advice.

Seminar objectives:

• contribute to bridging the existing gap between university research and decision making practical tasks in the field of data analysis;
• promote the exchange of experience between current and future professionals.

Seminar program

Small businesses in the CIS countries do not yet use data analysis for business development, determining correlations, searching for hidden patterns: entrepreneurs make do with reports from marketers and accountants. The leaders of small and partially medium-sized enterprises rely more on their intuition than on analysis. But at the same time, analytics has a huge potential: it helps to reduce costs and increase profits, make decisions faster and more objectively, optimize processes, better understand customers and improve the product.

An accountant will not replace an analyst

Small business executives often assume that marketing and accountant reports are a fairly accurate representation of a company's performance. But on the basis of dry statistics, it is very difficult to make a decision, and an error in calculations without specialized education is inevitable.

Case 1. Post-analysis of promotional campaigns. By the New Year, the entrepreneur announced a promotion in which certain goods were offered at a discount. After evaluating the revenue for the New Year period, he saw how sales increased, and was delighted with his resourcefulness. But let's take into account all the factors:

• Sales are especially strong on Friday, the day when revenue is maximum - this is a weekly trend.
• Compared to sales growth that usually occurs under New Year, then the gain is not so great.
• If you filter out promotional items, it turns out that sales figures have deteriorated.

Case 2. Study of turnover. At the store women's clothing difficulties with logistics: the goods in some warehouses are in short supply, and in others they lie for months. How to determine, without analyzing sales, how many trousers to bring to one region, and how many coats to send to another, while getting the maximum profit? To do this, you need to calculate the turnover, the ratio of the speed of sales and the average inventory for a certain period. To put it simply, the turnover is an indicator of how many days the store will sell the goods, how quickly the average stock is sold, how quickly the goods pay off. It is economically unprofitable to store large reserves, as this freezes capital and slows down development. If the stock is reduced, there may be a shortage, and the company will again lose profits. Where to find golden mean, the ratio at which the product does not stagnate in the warehouse, and at the same time you can give a certain guarantee that the customer will find the right unit in the store? To do this, the analyst should help you determine:

• desired turnover,
• turnover dynamics.

When settling with suppliers with a delay, you must also calculate the ratio of the credit line and turnover. Turnover in days = Average inventory* number of days / Turnover for this period.

Calculation of assortment balances and total turnover in stores helps to understand where it is necessary to move part of the goods. It is also worth calculating what turnover each unit of the assortment has in order to make a decision: markdown with reduced demand, re-order with increased demand, relocation to another warehouse. By category, you can develop a report on turnover in this form. It can be seen that T-shirts and jumpers are sold faster, but coats are sold for a long time. Can an ordinary accountant do this job? We doubt. At the same time, regular calculation of turnover and application of the results can increase profits by 8-10%.

In what areas is data analysis applicable?

1. Sales. It is important to understand why sales are going well (or badly), what are the dynamics. To solve this problem, it is necessary to investigate the factors influencing profit and revenue - for example, to analyze the length of the receipt and revenue per customer. Such factors can be investigated by groups of goods, seasons, stores. You can identify sales peaks and pits by analyzing returns, cancellations, and other transactions.
2. Finance. Monitoring of indicators is necessary for any financier to monitor cash flow and distribute assets across various business areas. This helps to evaluate the effectiveness of taxation and other parameters.
3. Marketing. Any marketing company needs forecasts and post-analysis of stocks. At the stage of developing the idea, it is necessary to determine the groups of goods (control and target) for which we are creating an offer. This is also a job for a data analyst, since an ordinary marketer does not have the necessary tools and skills for good analysis. For example, if for control group the amount of revenue and the number of buyers are equally greater in comparison with the target - the action did not work. To determine this, interval analysis is needed.
4. Control. Having leadership qualities is not enough for a company leader. Quantitative estimates in any case, the work of personnel is necessary for the competent management of the enterprise. It is important to understand the effectiveness of wage fund management, the ratio of wages and sales, as well as the efficiency of processes - for example, the workload of cash desks or the employment of loaders during the day. This helps to properly distribute working time.
5. Web analysis. The site needs to be properly promoted so that it becomes a sales channel, and this requires the right promotion strategy. This is where web analysis can help you. How to apply it? To study the behavior, age, gender and other characteristics of customers, activity on certain pages, clicks, traffic channel, mailing performance, etc. This will help improve the business and website.
6. Assortment management. ABC analysis is essential for assortment management. The analyst must distribute the product by characteristics in order to conduct this type of analysis and understand which product is the most profitable, which is the basis, and which should be discarded. To understand the stability of sales, it is good to conduct an XYZ analysis.
7. Logistics. More understanding about procurement, goods, their storage and availability will be given by the study of logistics indicators. Losses and needs of goods, inventory is also important to understand for successful business management.

These examples show how powerful data analysis is, even for small businesses. An experienced director will increase the company's profits and benefit from the most insignificant data, using data analysis correctly, and visual reports will greatly simplify the work of a manager.

Business intelligence and data analysis. Effective consulting is what is necessary for the qualitative development of any business. Solving existing problems and crises, preventing potential ones, finding ways to increase profits and efficiency in general: all this provides you with quality consulting.

The consulting process is complex, multi-stage, multi-level, there is no clear and universal approach to absolutely any business: the business context, its niche, industry, target audience, features and much more: all this affects how business processes will be diagnosed. Naturally, the final stage of consulting is preceded by many other pre-processes, such as preparing a task, describing business processes, business analytics, diagnosing the infrastructure in general and the IT infrastructure of the organization, in particular, data is analyzed, and based on this, a number of recommendations are created. . I must say that it is business analytics and data analysis that are the most important stages in the consulting process, they lead to the appropriate conclusions, it is on the basis of such an analysis that any recommendations are made.

Data analysis and business analytics: how to implement?

Qualitative analysis, in this case, cannot do without the presence of any quantitative metrics. That is, it is very desirable that some kind of automation be introduced into the work of the enterprise - business processes, relationships with customers, suppliers, intermediaries, so that document flow and all other processes are also automated. It is with a qualitative account of all the processes occurring within the business that reporting and further analytics are greatly facilitated.

How can you automate document flow, customer management and facilitate reporting?

The best option would be exclusive software designed to perform many tasks - from FB Consult. You are offered high-quality customer management systems - various kinds of CRM, designed for various business sectors, an effective document management solution - DocsVision, as well as software suitable for business analytics and data analysis, including - and for identifying dubious financial transactions - QlikView . The implementation of such solutions will significantly increase the efficiency of your business.

Affordable work with Big Data using visual analytics

Improve business intelligence and solve routine tasks using the information hidden in Big Data using the TIBCO Spotfire platform. It is the only platform that provides business users with an intuitive, user-friendly user interface that allows them to use the full range of Big Data analytics technologies without the need for IT professionals or special education.

The Spotfire interface makes it equally convenient to work with both small data sets and multi-terabyte clusters of big data: sensor readings, information from social networks, points of sale or geolocation sources. Users of all skill levels easily access rich dashboards and analytical workflows simply by using visualizations, which are graphical representations of the aggregation of billions of data points.

Predictive analytics is learning by doing based on shared company experience to make better informed decisions. Using Spotfire Predictive Analytics, you can discover new market trends from your business intelligence insights and take action to mitigate risk to improve management decisions.

Review

Connecting to Big Data for High-Performance Analytics

Spotfire offers three main types of analytics with seamless integration with Hadoop and other large data sources:

1. Data visualization on demand (On-Demand Analytics): built-in, user-configurable data connectors that simplify super-fast, interactive data visualization
2. Analysis in the database (In-Database Analytics): integration with the distributed computing platform, which allows you to make data calculations of any complexity based on big data.
3. In-Memory Analytics: Integration with a statistical analysis platform that pulls data directly from any data source, including traditional and new data sources.

Together, these integration methods represent a powerful combination of visual exploration and advanced analytics.
It allows business users to access, combine and analyze data from any data source with powerful, easy-to-use dashboards and workflows.

Big data connectors

Spotfire Big Data Connectors support all types of data access: In-datasource, In-memory and On-demand. Built-in Spotfire data connectors include:

• Certified Hadoop Data Connectors for Apache Hive, Apache Spark SQL, Cloudera Hive, Cloudera Impala, Databricks Cloud, Hortonworks, MapR Drill and Pivotal HAWQ
• Other certified big data connectors include Teradata, Teradata Aster and Netezza
• Connectors for historical and current data from sources such as OSI PI touch sensors

In-datasource distributed computing

In addition to Spotfire's handy visual selection of operations for SQL queries that access data distributed across data sources, Spotfire can create statistical and machine learning algorithms that operate within data sources and return only the results needed to create visualizations in the Spotfire system.

• Users work with dashboards with visual selection functionality that access scripts using the built-in features of the TERR language,
• TERR scripts invoke distributed computing functionality in conjunction with Map/Reduce, H2O, SparkR, or Fuzzy Logix,
• These applications in turn access systems with high efficiency like Hadoop or other data sources,
• TERR can be deployed as an advanced analytics engine on Hadoop nodes that are managed with MapReduce or Spark. The TERR language can also be used for Teradata data nodes.
• The results are visualized on Spotfire.

TERR for advanced analytics

TIBCO Enterprise Runtime for R (TERR) - TERR is a statistical package corporate level, which was developed by TIBCO to be fully compatible with the R language, implementing the company's years of experience in the analytical system associated with S +. This allows customers to continue developing applications and models not only using open source R, but also to integrate and deploy their R code on a commercially reliable platform without having to rewrite their code. TERR has higher efficiency and reliable memory management, provides more high speed processing data on large volumes in comparison with the open source R language.

Combining all functionality

Combining the aforementioned powerful functionality means that even for the most complex tasks requiring highly reliable analytics, users interact with simple and easy-to-use interactive workflows. This allows business users to visualize and analyze data, and share analytics results, without having to know the details of the data architecture that underpins business intelligence.

Example: Spotfire interface for configuring, running and visualizing the results of a model that characterizes lost cargo. Through this interface, business users can perform calculations using TERR and H2O (a distributed computing framework) on transaction and shipment data stored in Hadoop clusters.

Analytical space for big data

Advanced and predictive analytics

Users use Spotfire's visual selection dashboards to launch a rich set of advanced features that make it easy to make predictions, build models, and optimize them on the fly. Using big data, analysis can be done inside the data source (In-Datasource), returning only the aggregated information and results needed to create visualizations on the Spotfire platform.

Machine learning

A wide range of machine learning tools are available in Spotfire's list of built-in features that can be used with a single click. Statisticians have access to the program code written in the R language and can extend the functionality used. Machine learning functionality can be shared with other users for easy reuse.

Available following methods machine learning for continuous categorical variables on Spotfire and on TERR:

• Linear and logistic regression
• Decision trees, Random forest algorithm, Gradient boosting machines (GBM)
• Generalized linear (additive) models ( Generalized Additive Models)
• Neural networks

Content analysis

Spotfire provides analytics and data visualization, much of which has not been used before - it is unstructured text that is stored in sources such as documents, reports, notes CRM systems, site logs, publications in social networks and much more.

Location analytics

Layered maps high resolution are a great way to visualize big data. Spotfire's rich map functionality allows you to create maps with as many reference and functional layers as you need. Spotfire also gives you the ability to use sophisticated analytics while working with maps. In addition to geographical maps, the system creates maps to visualize user behavior, warehouses, production, raw materials and many other indicators.